Consumers of resources can select the resource based on the value of certain traits the resource possesses. For example, a racehorse may be selected based on its speed. Some consumers select resources based on the value of multiple traits, with the consumers trading off one trait with another based on the importance, or weight, the consumer assigns to that trait. Thus, a racehorse may not only be selected based on its speed, but also on its overall bone structure. A fast racehorse with brittle bones is not as valuable to a consumer interested in long term ownership as a slightly slower racehorse with stronger bones.
When a consumer starts the resource selection process, the consumer may not know all of the traits that the consumer will ultimately find important. Thus, initially, a consumer may simply look for the fastest racehorse, but over time, may learn that bone structure is a trait that they would like to consider.
The consumer may also assign a weight to each trait, but consumers are not always forthcoming in the weight assigned to each trait. The consumer may decide that bone structure is the most important trait, and speed is the second most important trait. However, when the consumer ultimately selects the horse, he may discover that speed is actually equally important to bone structure.
A consumer may initially determine that only certain values of traits are acceptable, but consumers can change their minds regarding such values. For example, the consumer may initially decide that a certain price for the horse is his or her maximum. However, presented with a very fast horse with excellent bone structure, the consumer may elect to spend more than their stated maximum.
In addition, the consumer may not realize that the consumer finds certain traits to be important. A consumer may find that consumer gravitating towards taller horses, when the height of the horse is not even on the list of traits the consumer believes are important to that consumer.
There are different methods that can be used for selecting a resource using a network. For example, a consumer at the consumer system may request a server to provide a list of all of the available resources subject to an initial selection criteria. The consumer can then sift through the profiles returned and select the most promising resources. This method may not work well when there are many resources, or if the consumers do not wish to be so actively involved in locating them.
Passive consumers might prefer having a far smaller, targeted set of resources provided to them based on a matching algorithm that uses their answers to a battery of questions. However, as noted above, the consumers may not be aware of things that are important to them, may not be cognizant of the weights and values of the characteristics they prefer, and may not want to answer the lengthy battery of questions that can be required to provide a truly targeted match.
What is needed is a system and method that can identify a more targeted group of resources than is available using a search, that can match the resources to the consumer's preferences, without requiring the consumers to answer a lengthy battery of questions, and can learn the consumer's preferences to provide better targeting of the available resources to the consumer's preferences, even those preferences the consumer may not realize they had.